title: Self-organized noise resistance of oscillatory neural networks with spike timing-dependent plasticity.
abstract: Intuitively one might expect independent noise to be a powerful tool for desynchronizing a population of synchronized neurons. We here show that, intriguingly, for oscillatory neural populations with adaptive synaptic weights governed by spike timing-dependent plasticity (STDP) the opposite is true. We found that the mean synaptic coupling in such systems increases dynamically in response to the increase of the noise intensity, and there is an optimal noise level, where the amount of synaptic coupling gets maximal in a resonance-like manner as found for the stochastic or coherence resonances, although the mechanism in our case is different. This constitutes a noise-induced self-organization of the synaptic connectivity, which effectively counteracts the desynchronizing impact of independent noise over a wide range of the noise intensity. Given the attempts to counteract neural synchrony underlying tinnitus with noisers and maskers, our results may be of clinical relevance.
authors.affiliation: Institute of Neuroscience and Medicine - Neuromodulation (INM-7), Research Center Jülich, 52425 Jülich, Germany.
authors.forename: Oleksandr V
authors.initials: OV
authors.lastname: Popovych
authors.forename: Serhiy
authors.initials: S
authors.lastname: Yanchuk
authors.forename: Peter A
authors.initials: PA
authors.lastname: Tass
mesh_terms: D000200:Action Potentials; D000465:Algorithms; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009473:Neuronal Plasticity; D009474:Neurons / Q000502:physiology*; D009622:Noise